主动学习与显式静电作用实现电解质的精确建模
Active learning and explicit electrostatics enable accurate modeling of electrolytes
AI总结：
提出基于D-最优性主动学习生成力矩张量势训练集，结合显式静电处理，实现碳酸酯类电解质的高精度分子动力学模拟，离子电导率与实验平均偏差小于11%。
AI中文摘要：
机器学习原子间势（MLIPs）在经典力场的效率下提供接近\textit{ab initio}的精度，使其在电解质建模中具有吸引力。收集多样化的训练集对其准确性和可靠性至关重要，并且可能需要显式处理强静电相互作用。在这项工作中，我们证明了基于D-最优性的主动学习可以自动生成力矩张量势（MTPs）的多样化训练集，从而实现对纯碳酸乙烯酯（EC）、碳酸甲乙酯（EMC）、它们的混合物以及LiPF$_6$溶液的可靠分子动力学模拟。得到的MTPs在各种EC/EMC组成下表现出优异的迁移性，产生的离子电导率与实验的平均偏差在11%以内。此外，我们通过使用固定或环境依赖电荷的电荷再分配方案增强MTP，评估了显式包含静电作用的影响。我们的结果表明，增强后的MTP以更少的参数达到与标准模型相同或更高的精度，而环境依赖电荷进一步提高了精度和模拟的稳定性。
英文摘要：
Machine learning interatomic potentials (MLIPs) offer near-\textit{ab initio} accuracy with the efficiency of classical force fields, making them attractive for modeling electrolytes. Collecting a diverse training set is essential for their accuracy and reliability, and explicit treatment of strong electrostatic interactions may be necessary. In this work, we demonstrated that D-optimality-based active learning can automatically generate diverse training sets for moment tensor potentials (MTPs), enabling reliable molecular dynamics simulations of pure ethylene carbonate (EC), ethyl methyl carbonate (EMC), their mixtures, and LiPF$_6$ solutions. The resulting MTPs exhibit excellent transferability across various EC/EMC compositions, producing ionic conductivities within 11\% mean deviations from experiments. In addition, we assessed the impact of explicitly incorporating electrostatics by augmenting MTP with charge redistribution schemes using either fixed or environment-dependent charges. Our results show that the augmented MTP achieves the same or higher accuracy than standard model with fewer parameters, while environment-dependent charges further improve accuracy and the stability of simulations.
